{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demo 4: How to use plotting functions"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "This demo will demonstrate the options for plotting projections and images on TIGRE. The functions have been in previous demos, but in here an exaustive explanation and usage of them is given.\n",
    "NOTE: if you havent already downloaded the tigre_demo_file and navigated to the correct directory, do so before continuing with this demo. \n",
    "--------------------------------------------------------------------------\n",
    "This file is part of the TIGRE Toolbox\n",
    "\n",
    "Copyright (c) 2015, University of Bath and\n",
    "                    CERN-European Organization for Nuclear Research\n",
    "                    All rights reserved.\n",
    "\n",
    "License:            Open Source under BSD.\n",
    "                    See the full license at\n",
    "                    https://github.com/CERN/TIGRE/license.txt\n",
    "\n",
    "Contact:            tigre.toolbox@gmail.com\n",
    "Codes:              https://github.com/CERN/TIGRE/\n",
    "--------------------------------------------------------------------------\n",
    "Coded by:          MATLAB (original code): Ander Biguri\n",
    "                   PYTHON : Reuben Lindroos,Sam Loescher"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tigre.geometry import TIGREParameters\n",
    "geo=TIGREParameters(high_quality=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data and generate projections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from _Ax import Ax\n",
    "from Test_data import data_loader\n",
    "# define angles\n",
    "angles=np.linspace(0,2*np.pi,dtype=np.float32)\n",
    "# load head phantom data\n",
    "head=data_loader.load_head_phantom(number_of_voxels=geo.nVoxel)\n",
    "# generate projections\n",
    "projections=Ax(head,geo,angles,'interpolated')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reconstruct image using OS-SART and FDK "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OS_SART algorithm in progress.\n",
      "Esitmated time until completetion (s): 2.46085\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aabhca20/Documents/TIGRE-python/Python/tigre_python_build/tigre/Utilities/filtering.py:56: UserWarning: no filter selected, using default ram_lak\n",
      "  warnings.warn('no filter selected, using default ram_lak')\n"
     ]
    }
   ],
   "source": [
    "from tigre.Algorithms.FDK import FDK \n",
    "from tigre.Algorithms.OS_SART import OS_SART\n",
    "from tigre.Utilities.plotImg import plotImg\n",
    "\n",
    "# OS_SART\n",
    "niter=50\n",
    "imgOSSART=OS_SART(projections,geo,angles,niter)\n",
    "\n",
    "# FDK \n",
    "imgfdk=FDK(projections,geo,angles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's use plotproj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Qz1V/gf4SSZ6g40wGO1/OaLCCj5fOvOz2nBc6b3XW8LJdv2DuvPNOAN0uXCrowskwmBBu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\nSEK4BUFQSEK4BUFQSEK4BUFQSP4/wqZYL82OQqIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f00e6ea2b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotproj plots the projection measured on the detector on each angle. \n",
    "\n",
    "from tigre.Utilities.plotproj import plotproj \n",
    "plotproj(projections)\n",
    "\n",
    "# another useful function is ppslice, which returns the projection at a single angle.\n",
    "# Default is at projections[len(angles)/2]\n",
    "\n",
    "from tigre.Utilities.plotproj import ppslice\n",
    "ppslice(projections,slice=35)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## what about plotImg?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class plotImg in module tigre.Utilities.plotImg:\n",
      "\n",
      "class plotImg\n",
      " |  plotImg(cube, dim) \n",
      " |      plots figure \n",
      " |  default: progressive in slices following\n",
      " |      axis (dim)\n",
      " |  Parameters \n",
      " |  ---------- \n",
      " |  cube : Any 3D numpy array \n",
      " |  \n",
      " |  dim : (\"X\",\"Y\",\"Z\",\"x\",\"y\",\"z\"), optional \n",
      " |         default is \"Z\"\n",
      " |         NOTE: string arguments!\n",
      " |  slice: int, optional\n",
      " |       returns page of matrix according to index\n",
      " |  Examples:\n",
      " |  ---------\n",
      " |  a=np.ones([3,3,3])\n",
      " |  plotImg(a)\n",
      " |  >>>returns plot along dim Z\n",
      " |  plotImg(a,dim=\"X\")\n",
      " |  >>>returns plot along dim X\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, cube, dim=None, slice=None)\n",
      " |  \n",
      " |  run(self)\n",
      " |  \n",
      " |  run_plot(self)\n",
      " |  \n",
      " |  slicer(self)\n",
      "\n"
     ]
    },
    {
     "data": {
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//d8fao90QxThk/PlsUXH2jkH9AoxrULKzmvZFovb5W2RJO2hEUUSZdts0xRF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\nlEiZftnRPo0MwhybaQ3OAekX18862Sf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f00e612f450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<tigre.Utilities.plotImg.plotImg instance at 0x7f00e601ab48>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plotImg plots the final reconstruction of the 3D array. Dim defaults to z, but can be\n",
    "# changed accordingly\n",
    "from tigre.Utilities.plotImg import plotImg\n",
    "# For more info on what parameters to use, type:\n",
    "help(plotImg)\n",
    "plotImg(np.hstack((imgOSSART,imgfdk)),slice=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
